results/orthofinder/output/Orthogroup_Sequences/OG0000001.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000002.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000003.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000004.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000005.fa

results/orthofinder/orthosnap/OG0000000/OG0000000_orthosnap_0.fa

results/alignment/trimmed_protein/OG0000002.trimmed.protein.alignment.fa
results/alignment/trimmed_protein/OG0000005.trimmed.protein.alignment.fa

General Characteristics
=======================
5	Number of taxa
795	Alignment length
59	Parsimony informative sites
59	Variable sites
716	Constant sites

Character Frequencies
=====================
Y	146
W	42
V	283
T	199
S	159
R	181
Q	193
P	224
N	185
M	64
L	347
K	247
I	254
H	81
G	342
F	192
E	226
D	193
C	64
A	306
-	47

IQ-TREE 2.2.0.3 COVID-edition built Sep  5 2022

Input file name: results/supermatrix/supermatrix.protein.fa
Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates)
Random seed number: 214413

REFERENCES
----------

To cite IQ-TREE please use:

Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf,
Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020)
IQ-TREE 2: New models and efficient methods for phylogenetic inference
in the genomic era. Mol. Biol. Evol., in press.
https://doi.org/10.1093/molbev/msaa015

To cite ModelFinder please use: 

Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler,
and Lars S Jermiin (2017) ModelFinder: Fast model selection for
accurate phylogenetic estimates. Nature Methods, 14:587–589.
https://doi.org/10.1038/nmeth.4285

Since you used ultrafast bootstrap (UFBoot) please also cite: 

Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh,
and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap
approximation. Mol. Biol. Evol., 35:518–522.
https://doi.org/10.1093/molbev/msx281

SEQUENCE ALIGNMENT
------------------

Input data: 5 sequences with 795 amino-acid sites
Number of constant sites: 568 (= 71.4465% of all sites)
Number of invariant (constant or ambiguous constant) sites: 568 (= 71.4465% of all sites)
Number of parsimony informative sites: 59
Number of distinct site patterns: 233

ModelFinder
-----------

Best-fit model according to BIC: cpREV+G4

List of models sorted by BIC scores: 

Model                  LogL         AIC      w-AIC        AICc     w-AICc         BIC      w-BIC
cpREV+G4          -4264.235    8544.470 +     0.28    8544.653 +    0.305    8581.896 +    0.758
cpREV+I+G4        -4262.935    8543.870 +    0.378    8544.100 +    0.403    8585.975 +   0.0987
LG+G4             -4266.340    8548.680 -   0.0341    8548.863 -   0.0372    8586.107 +   0.0924
LG+I+G4           -4263.605    8545.210 +    0.193    8545.439 +    0.206    8587.315 +   0.0505
Q.pfam+G4         -4274.081    8564.163 - 1.48e-05    8564.346 - 1.62e-05    8601.590 - 4.01e-05
LG+I              -4274.116    8564.233 - 1.43e-05    8564.416 - 1.56e-05    8601.659 - 3.88e-05
Q.pfam+I+G4       -4271.452    8560.904 - 7.56e-05    8561.133 - 8.06e-05    8603.009 - 1.97e-05
WAG+G4            -4276.694    8569.388 - 1.09e-06    8569.572 - 1.19e-06    8606.815 - 2.94e-06
WAG+I+G4          -4275.346    8568.692 - 1.54e-06    8568.921 - 1.64e-06    8610.797 - 4.02e-07
VT+G4             -4278.977    8573.953 - 1.11e-07    8574.136 - 1.21e-07    8611.380 -    3e-07
VT+I+G4           -4277.863    8573.727 - 1.24e-07    8573.956 - 1.32e-07    8615.832 - 3.24e-08
rtREV+G4          -4288.723    8593.447 - 6.49e-12    8593.630 - 7.07e-12    8630.874 - 1.76e-11
rtREV+I+G4        -4286.522    8591.044 - 2.16e-11    8591.273 -  2.3e-11    8633.149 - 5.63e-12
Q.plant+I+G4      -4286.649    8591.299 -  1.9e-11    8591.528 - 2.02e-11    8633.404 - 4.96e-12
JTTDCMut+G4       -4290.078    8596.156 - 1.67e-12    8596.339 - 1.82e-12    8633.583 - 4.53e-12
Blosum62+G4       -4290.417    8596.834 - 1.19e-12    8597.017 -  1.3e-12    8634.261 - 3.23e-12
JTTDCMut+I+G4     -4287.253    8592.507 - 1.04e-11    8592.736 - 1.11e-11    8634.612 - 2.71e-12
JTT+G4            -4290.924    8597.849 - 7.18e-13    8598.032 - 7.83e-13    8635.275 - 1.94e-12
Q.plant+G4        -4291.377    8598.753 - 4.57e-13    8598.937 - 4.98e-13    8636.180 - 1.24e-12
JTT+I+G4          -4288.113    8594.227 - 4.39e-12    8594.456 - 4.68e-12    8636.332 - 1.15e-12
Blosum62+I+G4     -4289.528    8597.055 - 1.07e-12    8597.284 - 1.14e-12    8639.160 - 2.79e-13
Q.yeast+I+G4      -4292.732    8603.464 - 4.33e-14    8603.693 - 4.62e-14    8645.569 - 1.13e-14
PMB+G4            -4296.492    8608.984 - 2.74e-15    8609.167 - 2.99e-15    8646.411 - 7.43e-15
Q.yeast+G4        -4296.496    8608.992 - 2.73e-15    8609.175 - 2.98e-15    8646.419 -  7.4e-15
PMB+I+G4          -4295.845    8609.691 - 1.93e-15    8609.920 - 2.05e-15    8651.796 - 5.03e-16
Q.insect+I+G4     -4299.971    8617.941 - 3.11e-17    8618.171 - 3.32e-17    8660.046 - 8.12e-18
Q.insect+G4       -4303.627    8623.254 - 2.18e-18    8623.437 - 2.38e-18    8660.681 - 5.92e-18
cpREV+F+G4        -4247.364    8548.727 -   0.0333    8550.698 -   0.0149    8675.042 -  4.5e-21
LG+F+G4           -4249.084    8552.168 -  0.00596    8554.139 -  0.00266    8678.483 - 8.06e-22
cpREV+F+I+G4      -4246.056    8548.113 -   0.0453    8550.233 -   0.0188    8679.106 -  5.9e-22
LG+F+I+G4         -4246.511    8549.022 -   0.0287    8551.142 -   0.0119    8680.016 - 3.75e-22
Dayhoff+G4        -4313.542    8643.085 - 1.08e-22    8643.268 - 1.18e-22    8680.512 - 2.92e-22
DCMut+G4          -4313.567    8643.135 - 1.05e-22    8643.318 - 1.15e-22    8680.561 - 2.85e-22
Dayhoff+I+G4      -4310.935    8639.869 - 5.39e-22    8640.099 - 5.74e-22    8681.974 - 1.41e-22
DCMut+I+G4        -4310.984    8639.968 - 5.13e-22    8640.197 - 5.46e-22    8682.073 - 1.34e-22
Q.pfam+F+G4       -4253.010    8560.020 - 0.000118    8561.991 - 5.25e-05    8686.335 - 1.59e-23
WAG+F+G4          -4253.474    8560.948 -  7.4e-05    8562.919 -  3.3e-05    8687.263 - 9.99e-24
Q.pfam+F+I+G4     -4250.465    8556.930 - 0.000551    8559.050 - 0.000228    8687.924 - 7.18e-24
mtZOA+F+I+G4      -4251.120    8558.239 - 0.000287    8560.359 - 0.000119    8689.233 - 3.73e-24
mtZOA+F+G4        -4255.408    8564.815 - 1.07e-05    8566.786 - 4.77e-06    8691.130 - 1.45e-24
WAG+F+I+G4        -4252.112    8560.224 - 0.000106    8562.344 -  4.4e-05    8691.217 - 1.38e-24
mtART+F+I+G4      -4253.942    8563.884 -  1.7e-05    8566.004 - 7.05e-06    8694.877 - 2.22e-25
mtInv+F+G4        -4257.685    8569.369 -  1.1e-06    8571.341 - 4.89e-07    8695.685 - 1.48e-25
mtInv+F+I+G4      -4254.766    8565.531 - 7.48e-06    8567.652 -  3.1e-06    8696.525 - 9.74e-26
mtART+F+G4        -4258.168    8570.336 - 6.77e-07    8572.308 - 3.02e-07    8696.652 - 9.14e-26
rtREV+F+G4        -4259.244    8572.489 - 2.31e-07    8574.460 - 1.03e-07    8698.804 - 3.12e-26
rtREV+F+I+G4      -4257.099    8570.198 - 7.25e-07    8572.318 -    3e-07    8701.192 - 9.45e-27
Q.yeast+F+I+G4    -4258.571    8573.141 - 1.66e-07    8575.261 - 6.89e-08    8704.135 - 2.17e-27
Q.yeast+F+G4      -4261.964    8577.929 - 1.52e-08    8579.900 - 6.78e-09    8704.244 - 2.05e-27
Q.plant+F+I+G4    -4261.841    8579.682 - 6.32e-09    8581.802 - 2.62e-09    8710.676 - 8.24e-29
Q.plant+F+G4      -4266.457    8586.915 -  1.7e-10    8588.886 - 7.58e-11    8713.230 -  2.3e-29
mtREV+F+G4        -4266.466    8586.932 - 1.69e-10    8588.903 - 7.51e-11    8713.247 - 2.28e-29
VT+F+G4           -4267.014    8588.028 - 9.74e-11    8589.999 - 4.34e-11    8714.343 - 1.32e-29
mtREV+F+I+G4      -4263.923    8583.846 - 7.88e-10    8585.966 - 3.26e-10    8714.839 - 1.03e-29
Q.insect+F+G4     -4268.562    8591.124 - 2.07e-11    8593.095 - 9.24e-12    8717.439 -  2.8e-30
Q.insect+F+I+G4   -4265.282    8586.563 - 2.03e-10    8588.683 - 8.39e-11    8717.557 - 2.64e-30
VT+F+I+G4         -4266.007    8588.013 - 9.81e-11    8590.133 - 4.06e-11    8719.007 - 1.28e-30
PMB+F+G4          -4272.250    8598.500 - 5.18e-13    8600.471 - 2.31e-13    8724.815 - 7.01e-32
HIVb+I+G4         -4332.623    8683.246 - 2.05e-31    8683.475 - 2.19e-31    8725.351 - 5.36e-32
JTTDCMut+F+G4     -4272.902    8599.804 -  2.7e-13    8601.775 -  1.2e-13    8726.119 - 3.65e-32
JTTDCMut+F+I+G4   -4270.101    8596.202 - 1.64e-12    8598.322 - 6.77e-13    8727.195 - 2.13e-32
Dayhoff+F+G4      -4273.453    8600.906 - 1.56e-13    8602.878 - 6.94e-14    8727.222 -  2.1e-32
DCMut+F+G4        -4273.485    8600.970 - 1.51e-13    8602.941 - 6.72e-14    8727.285 - 2.04e-32
JTT+F+G4          -4273.601    8601.202 - 1.34e-13    8603.173 - 5.99e-14    8727.517 - 1.81e-32
Dayhoff+F+I+G4    -4270.621    8597.242 - 9.72e-13    8599.362 - 4.03e-13    8728.235 - 1.27e-32
DCMut+F+I+G4      -4270.680    8597.360 - 9.17e-13    8599.480 -  3.8e-13    8728.353 - 1.19e-32
HIVb+G4           -4337.534    8691.069 - 4.11e-33    8691.252 - 4.48e-33    8728.495 - 1.11e-32
JTT+F+I+G4        -4270.827    8597.654 - 7.91e-13    8599.774 - 3.28e-13    8728.647 - 1.03e-32
PMB+F+I+G4        -4271.626    8599.252 - 3.56e-13    8601.372 - 1.47e-13    8730.246 - 4.64e-33
Blosum62+F+G4     -4276.695    8607.391 - 6.08e-15    8609.362 - 2.71e-15    8733.706 - 8.22e-34
mtMet+F+I+G4      -4273.719    8603.439 - 4.39e-14    8605.559 - 1.82e-14    8734.432 - 5.72e-34
mtMet+F+G4        -4277.978    8609.955 - 1.69e-15    8611.927 - 7.52e-16    8736.271 - 2.28e-34
Blosum62+F+I+G4   -4275.878    8607.755 - 5.07e-15    8609.875 -  2.1e-15    8738.749 -  6.6e-35
FLU+I+G4          -4341.240    8700.481 - 3.71e-35    8700.710 - 3.96e-35    8742.586 -  9.7e-36
FLU+G4            -4346.657    8709.314 - 4.48e-37    8709.497 - 4.89e-37    8746.741 - 1.21e-36
Q.mammal+I+G4     -4344.882    8707.764 - 9.73e-37    8707.993 - 1.04e-36    8749.869 - 2.54e-37
Q.bird+I+G4       -4345.555    8709.110 - 4.97e-37    8709.339 - 5.29e-37    8751.215 -  1.3e-37
Q.mammal+G4       -4350.388    8716.777 - 1.07e-38    8716.960 - 1.17e-38    8754.204 - 2.91e-38
Q.bird+G4         -4352.531    8721.063 - 1.26e-39    8721.246 - 1.37e-39    8758.490 - 3.41e-39
FLU+F+I+G4        -4295.811    8647.622 - 1.12e-23    8649.742 - 4.62e-24    8778.616 - 1.45e-43
FLU+F+G4          -4300.960    8655.921 - 1.76e-25    8657.892 - 7.85e-26    8782.236 - 2.38e-44
mtVer+F+I+G4      -4301.125    8658.250 -  5.5e-26    8660.370 - 2.28e-26    8789.243 - 7.16e-46
Q.mammal+F+I+G4   -4302.004    8660.008 - 2.28e-26    8662.128 - 9.45e-27    8791.002 - 2.97e-46
mtMAM+F+I+G4      -4302.137    8660.274 -    2e-26    8662.394 - 8.27e-27    8791.268 -  2.6e-46
HIVb+F+I+G4       -4302.523    8661.045 - 1.36e-26    8663.166 - 5.62e-27    8792.039 - 1.77e-46
HIVb+F+G4         -4307.488    8668.976 - 2.58e-28    8670.947 - 1.15e-28    8795.291 - 3.48e-47
Q.mammal+F+G4     -4307.577    8669.153 - 2.36e-28    8671.124 - 1.05e-28    8795.468 - 3.19e-47
mtVer+F+G4        -4308.190    8670.379 - 1.28e-28    8672.350 -  5.7e-29    8796.694 - 1.73e-47
mtMAM+F+G4        -4308.475    8670.949 -  9.6e-29    8672.921 - 4.28e-29    8797.265 -  1.3e-47
LG                -4378.052    8770.104 - 2.83e-50    8770.246 - 3.15e-50    8802.852 - 7.94e-49
Q.bird+F+I+G4     -4311.654    8679.308 - 1.47e-30    8681.428 - 6.09e-31    8810.302 - 1.92e-50
Q.bird+F+G4       -4318.788    8691.576 - 3.19e-33    8693.547 - 1.42e-33    8817.891 - 4.31e-52
FLAVI+I+G4        -4392.585    8803.169 - 1.87e-57    8803.398 - 1.99e-57    8845.274 - 4.88e-58
FLAVI+G4          -4401.151    8818.302 - 9.66e-61    8818.486 - 1.05e-60    8855.729 - 2.62e-60
FLAVI+F+I+G4      -4334.743    8725.486 - 1.38e-40    8727.607 - 5.71e-41    8856.480 -  1.8e-60
FLAVI+F+G4        -4343.385    8740.771 - 6.62e-44    8742.742 - 2.95e-44    8867.086 - 8.95e-63
mtZOA+I+G4        -4408.102    8834.205 -  3.4e-64    8834.434 - 3.63e-64    8876.310 - 8.89e-65
mtZOA+G4          -4413.222    8842.444 - 5.53e-66    8842.627 - 6.03e-66    8879.870 -  1.5e-65
mtART+I+G4        -4433.667    8885.334 - 2.69e-75    8885.563 - 2.86e-75    8927.439 - 7.02e-76
mtART+G4          -4438.666    8893.332 - 4.93e-77    8893.515 - 5.37e-77    8930.759 - 1.33e-76
HIVw+I+G4         -4438.926    8895.852 -  1.4e-77    8896.081 - 1.49e-77    8937.957 - 3.65e-78
HIVw+G4           -4447.382    8910.764 - 8.08e-81    8910.947 - 8.81e-81    8948.191 - 2.19e-80
HIVw+F+I+G4       -4381.920    8819.839 - 4.48e-61    8821.959 - 1.86e-61    8950.833 - 5.84e-81
mtREV+I+G4        -4448.758    8915.516 - 7.51e-82    8915.745 -    8e-82    8957.621 - 1.96e-82
mtREV+G4          -4452.789    8921.578 - 3.62e-83    8921.762 - 3.95e-83    8959.005 - 9.81e-83
HIVw+F+G4         -4389.607    8833.215 - 5.58e-64    8835.186 - 2.49e-64    8959.530 - 7.55e-83
mtMet+I+G4        -4462.631    8943.263 - 7.09e-88    8943.492 - 7.55e-88    8985.368 - 1.85e-88
mtMet+G4          -4468.619    8953.238 - 4.83e-90    8953.421 - 5.27e-90    8990.665 - 1.31e-89
mtMAM+I+G4        -4484.229    8986.458 - 2.96e-97    8986.687 - 3.15e-97    9028.563 - 7.72e-98
mtMAM+G4          -4491.262    8998.525 - 7.08e-100    8998.708 - 7.73e-100    9035.952 - 1.92e-99
mtVer+I+G4        -4491.737    9001.473 - 1.62e-100    9001.703 - 1.73e-100    9043.579 - 4.24e-101
mtVer+G4          -4499.986    9015.973 - 1.15e-103    9016.156 - 1.26e-103    9053.400 - 3.12e-103
mtInv+I+G4        -4497.122    9012.243 - 7.44e-103    9012.473 - 7.92e-103    9054.349 - 1.94e-103
mtInv+G4          -4501.408    9018.816 - 2.78e-104    9018.999 - 3.03e-104    9056.243 - 7.53e-104

AIC, w-AIC   : Akaike information criterion scores and weights.
AICc, w-AICc : Corrected AIC scores and weights.
BIC, w-BIC   : Bayesian information criterion scores and weights.

Plus signs denote the 95% confidence sets.
Minus signs denote significant exclusion.

SUBSTITUTION PROCESS
--------------------

Model of substitution: cpREV+G4

State frequencies: (model)

Model of rate heterogeneity: Gamma with 4 categories
Gamma shape alpha: 0.2552

 Category  Relative_rate  Proportion
  1         0.0024         0.2500
  2         0.0705         0.2500
  3         0.5124         0.2500
  4         3.4148         0.2500
Relative rates are computed as MEAN of the portion of the Gamma distribution falling in the category.

MAXIMUM LIKELIHOOD TREE
-----------------------

Log-likelihood of the tree: -4264.2348 (s.e. 105.6605)
Unconstrained log-likelihood (without tree): -3328.5904
Number of free parameters (#branches + #model parameters): 8
Akaike information criterion (AIC) score: 8544.4696
Corrected Akaike information criterion (AICc) score: 8544.6528
Bayesian information criterion (BIC) score: 8581.8963

Total tree length (sum of branch lengths): 0.6566
Sum of internal branch lengths: 0.1265 (19.2602% of tree length)

NOTE: Tree is UNROOTED although outgroup taxon 'Avrainvillea_mazei_HV02664' is drawn at root
Numbers in parentheses are  ultrafast bootstrap support (%)

+---------------------------------------------Avrainvillea_mazei_HV02664
|
+-----------------------------------Bryopsis_plumosa_WEST4718
|
|                       +----------------------------------Caulerpa_cliftonii_HV03798
+-----------------------| (100)
                        |         +-----------------------Chlorodesmis_fastigiata_HV03865
                        +---------| (93)
                                  +-----Flabellia_petiolata_HV01202

Tree in newick format:

(Avrainvillea_mazei_HV02664:0.1661897690,Bryopsis_plumosa_WEST4718:0.1298038579,(Caulerpa_cliftonii_HV03798:0.1248392241,(Chlorodesmis_fastigiata_HV03865:0.0854630913,Flabellia_petiolata_HV01202:0.0238380002)93:0.0387725358)100:0.0876893201);

CONSENSUS TREE
--------------

Consensus tree is constructed from 1000 bootstrap trees
Log-likelihood of consensus tree: -4264.234814
Robinson-Foulds distance between ML tree and consensus tree: 0

Branches with support >0.000000% are kept (extended consensus)
Branch lengths are optimized by maximum likelihood on original alignment
Numbers in parentheses are bootstrap supports (%)

+---------------------------------------------Avrainvillea_mazei_HV02664
|
+-----------------------------------Bryopsis_plumosa_WEST4718
|
|                       +----------------------------------Caulerpa_cliftonii_HV03798
+-----------------------| (100)
                        |         +-----------------------Chlorodesmis_fastigiata_HV03865
                        +---------| (93)
                                  +-----Flabellia_petiolata_HV01202


Consensus tree in newick format: 

(Avrainvillea_mazei_HV02664:0.1661857555,Bryopsis_plumosa_WEST4718:0.1298019354,(Caulerpa_cliftonii_HV03798:0.1248533989,(Chlorodesmis_fastigiata_HV03865:0.0854959744,Flabellia_petiolata_HV01202:0.0237923437)93:0.0387594158)100:0.0876886082);

TIME STAMP
----------

Date and time: Fri Jan 19 23:44:31 2024
Total CPU time used: 10.876028 seconds (0h:0m:10s)
Total wall-clock time used: 1.064414978 seconds (0h:0m:1s)

IQ-TREE multicore version 2.2.0.3 COVID-edition for Mac OS X 64-bit built Sep  5 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.

Host:    9350L-152779-M (AVX2, FMA3, 16 GB RAM)
Command: iqtree2 -s results/supermatrix/supermatrix.protein.fa -bb 1000 -m TEST -nt 12 -redo
Seed:    214413 (Using SPRNG - Scalable Parallel Random Number Generator)
Time:    Fri Jan 19 23:44:30 2024
Kernel:  AVX+FMA - 12 threads (12 CPU cores detected)

Reading alignment file results/supermatrix/supermatrix.protein.fa ... Fasta format detected
Reading fasta file: done in 0.000130177 secs using 80.66% CPU
Alignment most likely contains protein sequences
Constructing alignment: done in 0.00132704 secs using 1131% CPU
Alignment has 5 sequences with 795 columns, 233 distinct patterns
59 parsimony-informative, 168 singleton sites, 568 constant sites
                                 Gap/Ambiguity  Composition  p-value
Analyzing sequences: done in 1.50204e-05 secs using 73.23% CPU
   1  Avrainvillea_mazei_HV02664         1.01%    passed     99.95%
   2  Bryopsis_plumosa_WEST4718          0.38%    passed    100.00%
   3  Caulerpa_cliftonii_HV03798         0.88%    passed    100.00%
   4  Chlorodesmis_fastigiata_HV03865    1.38%    passed    100.00%
   5  Flabellia_petiolata_HV01202        2.26%    passed    100.00%
****  TOTAL                              1.18%  0 sequences failed composition chi2 test (p-value<5%; df=19)
Checking for duplicate sequences: done in 5.29289e-05 secs using 710.4% CPU


Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.000 seconds
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Perform fast likelihood tree search using LG+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 0.00155616 secs using 1107% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -4263.611
Optimal log-likelihood: -4263.607
Proportion of invariable sites: 0.358
Gamma shape alpha: 0.536
Parameters optimization took 1 rounds (0.002 sec)
Time for fast ML tree search: 0.012 seconds

NOTE: ModelFinder requires 1 MB RAM!
ModelFinder will test up to 224 protein models (sample size: 795) ...
 No. Model         -LnL         df  AIC          AICc         BIC
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  1  LG            4378.052     7   8770.104     8770.246     8802.852
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  2  LG+I          4274.116     8   8564.233     8564.416     8601.659
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  3  LG+G4         4266.340     8   8548.680     8548.863     8586.107
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  4  LG+I+G4       4263.605     9   8545.210     8545.439     8587.315
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  7  LG+F+G4       4249.084     27  8552.168     8554.139     8678.483
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  8  LG+F+I+G4     4246.511     28  8549.022     8551.142     8680.016
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 11  WAG+G4        4276.694     8   8569.388     8569.572     8606.815
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 12  WAG+I+G4      4275.346     9   8568.692     8568.921     8610.797
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 15  WAG+F+G4      4253.474     27  8560.948     8562.919     8687.263
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 16  WAG+F+I+G4    4252.112     28  8560.224     8562.344     8691.217
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 19  JTT+G4        4290.924     8   8597.849     8598.032     8635.275
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 20  JTT+I+G4      4288.113     9   8594.227     8594.456     8636.332
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 23  JTT+F+G4      4273.601     27  8601.202     8603.173     8727.517
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 24  JTT+F+I+G4    4270.827     28  8597.654     8599.774     8728.647
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 27  Q.pfam+G4     4274.081     8   8564.163     8564.346     8601.590
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 28  Q.pfam+I+G4   4271.452     9   8560.904     8561.133     8603.009
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 31  Q.pfam+F+G4   4253.010     27  8560.020     8561.991     8686.335
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 32  Q.pfam+F+I+G4 4250.465     28  8556.930     8559.050     8687.924
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 35  Q.bird+G4     4352.531     8   8721.063     8721.246     8758.490
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 36  Q.bird+I+G4   4345.555     9   8709.110     8709.339     8751.215
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 39  Q.bird+F+G4   4318.788     27  8691.576     8693.547     8817.891
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 40  Q.bird+F+I+G4 4311.654     28  8679.308     8681.428     8810.302
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 43  Q.mammal+G4   4350.388     8   8716.777     8716.960     8754.204
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 44  Q.mammal+I+G4 4344.882     9   8707.764     8707.993     8749.869
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 47  Q.mammal+F+G4 4307.577     27  8669.153     8671.124     8795.468
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 48  Q.mammal+F+I+G4 4302.004     28  8660.008     8662.128     8791.002
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 51  Q.insect+G4   4303.627     8   8623.254     8623.437     8660.681
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 52  Q.insect+I+G4 4299.971     9   8617.941     8618.171     8660.046
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 55  Q.insect+F+G4 4268.562     27  8591.124     8593.095     8717.439
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 56  Q.insect+F+I+G4 4265.282     28  8586.563     8588.683     8717.557
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 59  Q.plant+G4    4291.377     8   8598.753     8598.937     8636.180
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 60  Q.plant+I+G4  4286.649     9   8591.299     8591.528     8633.404
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 63  Q.plant+F+G4  4266.457     27  8586.915     8588.886     8713.230
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 64  Q.plant+F+I+G4 4261.841     28  8579.682     8581.802     8710.676
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 67  Q.yeast+G4    4296.496     8   8608.992     8609.175     8646.419
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 68  Q.yeast+I+G4  4292.732     9   8603.464     8603.693     8645.569
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 71  Q.yeast+F+G4  4261.964     27  8577.929     8579.900     8704.244
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 72  Q.yeast+F+I+G4 4258.571     28  8573.141     8575.261     8704.135
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 75  JTTDCMut+G4   4290.078     8   8596.156     8596.339     8633.583
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 76  JTTDCMut+I+G4 4287.253     9   8592.507     8592.736     8634.612
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 79  JTTDCMut+F+G4 4272.902     27  8599.804     8601.775     8726.119
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 80  JTTDCMut+F+I+G4 4270.101     28  8596.202     8598.322     8727.195
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 83  DCMut+G4      4313.567     8   8643.135     8643.318     8680.561
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 84  DCMut+I+G4    4310.984     9   8639.968     8640.197     8682.073
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 87  DCMut+F+G4    4273.485     27  8600.970     8602.941     8727.285
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 88  DCMut+F+I+G4  4270.680     28  8597.360     8599.480     8728.353
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 91  VT+G4         4278.977     8   8573.953     8574.136     8611.380
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 92  VT+I+G4       4277.863     9   8573.727     8573.956     8615.832
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 95  VT+F+G4       4267.014     27  8588.028     8589.999     8714.343
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 96  VT+F+I+G4     4266.007     28  8588.013     8590.133     8719.007
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 99  PMB+G4        4296.492     8   8608.984     8609.167     8646.411
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
100  PMB+I+G4      4295.845     9   8609.691     8609.920     8651.796
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
103  PMB+F+G4      4272.250     27  8598.500     8600.471     8724.815
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
104  PMB+F+I+G4    4271.626     28  8599.252     8601.372     8730.246
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
107  Blosum62+G4   4290.417     8   8596.834     8597.017     8634.261
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
108  Blosum62+I+G4 4289.528     9   8597.055     8597.284     8639.160
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
111  Blosum62+F+G4 4276.695     27  8607.391     8609.362     8733.706
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
112  Blosum62+F+I+G4 4275.878     28  8607.755     8609.875     8738.749
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
115  Dayhoff+G4    4313.542     8   8643.085     8643.268     8680.512
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
116  Dayhoff+I+G4  4310.935     9   8639.869     8640.099     8681.974
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
119  Dayhoff+F+G4  4273.453     27  8600.906     8602.878     8727.222
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
120  Dayhoff+F+I+G4 4270.621     28  8597.242     8599.362     8728.235
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
123  mtREV+G4      4452.789     8   8921.578     8921.762     8959.005
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
124  mtREV+I+G4    4448.758     9   8915.516     8915.745     8957.621
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
127  mtREV+F+G4    4266.466     27  8586.932     8588.903     8713.247
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
128  mtREV+F+I+G4  4263.923     28  8583.846     8585.966     8714.839
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
131  mtART+G4      4438.666     8   8893.332     8893.515     8930.759
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
132  mtART+I+G4    4433.667     9   8885.334     8885.563     8927.439
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
135  mtART+F+G4    4258.168     27  8570.336     8572.308     8696.652
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
136  mtART+F+I+G4  4253.942     28  8563.884     8566.004     8694.877
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
139  mtZOA+G4      4413.222     8   8842.444     8842.627     8879.870
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
140  mtZOA+I+G4    4408.102     9   8834.205     8834.434     8876.310
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
143  mtZOA+F+G4    4255.408     27  8564.815     8566.786     8691.130
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
144  mtZOA+F+I+G4  4251.120     28  8558.239     8560.359     8689.233
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
147  mtMet+G4      4468.619     8   8953.238     8953.421     8990.665
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
148  mtMet+I+G4    4462.631     9   8943.263     8943.492     8985.368
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
151  mtMet+F+G4    4277.978     27  8609.955     8611.927     8736.271
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
152  mtMet+F+I+G4  4273.719     28  8603.439     8605.559     8734.432
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
155  mtVer+G4      4499.986     8   9015.973     9016.156     9053.400
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
156  mtVer+I+G4    4491.737     9   9001.473     9001.703     9043.579
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
159  mtVer+F+G4    4308.190     27  8670.379     8672.350     8796.694
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
160  mtVer+F+I+G4  4301.125     28  8658.250     8660.370     8789.243
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
163  mtInv+G4      4501.408     8   9018.816     9018.999     9056.243
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
164  mtInv+I+G4    4497.122     9   9012.243     9012.473     9054.349
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
167  mtInv+F+G4    4257.685     27  8569.369     8571.341     8695.685
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
168  mtInv+F+I+G4  4254.766     28  8565.531     8567.652     8696.525
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
171  mtMAM+G4      4491.262     8   8998.525     8998.708     9035.952
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
172  mtMAM+I+G4    4484.229     9   8986.458     8986.687     9028.563
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
175  mtMAM+F+G4    4308.475     27  8670.949     8672.921     8797.265
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
176  mtMAM+F+I+G4  4302.137     28  8660.274     8662.394     8791.268
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
179  FLAVI+G4      4401.151     8   8818.302     8818.486     8855.729
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
180  FLAVI+I+G4    4392.585     9   8803.169     8803.398     8845.274
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
183  FLAVI+F+G4    4343.385     27  8740.771     8742.742     8867.086
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
184  FLAVI+F+I+G4  4334.743     28  8725.486     8727.607     8856.480
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
187  HIVb+G4       4337.534     8   8691.069     8691.252     8728.495
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
188  HIVb+I+G4     4332.623     9   8683.246     8683.475     8725.351
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
191  HIVb+F+G4     4307.488     27  8668.976     8670.947     8795.291
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
192  HIVb+F+I+G4   4302.523     28  8661.045     8663.166     8792.039
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
195  HIVw+G4       4447.382     8   8910.764     8910.947     8948.191
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
196  HIVw+I+G4     4438.926     9   8895.852     8896.081     8937.957
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
199  HIVw+F+G4     4389.607     27  8833.215     8835.186     8959.530
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
200  HIVw+F+I+G4   4381.920     28  8819.839     8821.959     8950.833
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
203  FLU+G4        4346.657     8   8709.314     8709.497     8746.741
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
204  FLU+I+G4      4341.240     9   8700.481     8700.710     8742.586
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
207  FLU+F+G4      4300.960     27  8655.921     8657.892     8782.236
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
208  FLU+F+I+G4    4295.811     28  8647.622     8649.742     8778.616
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
211  rtREV+G4      4288.723     8   8593.447     8593.630     8630.874
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
212  rtREV+I+G4    4286.522     9   8591.044     8591.273     8633.149
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
215  rtREV+F+G4    4259.244     27  8572.489     8574.460     8698.804
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
216  rtREV+F+I+G4  4257.099     28  8570.198     8572.318     8701.192
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
219  cpREV+G4      4264.235     8   8544.470     8544.653     8581.896
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
220  cpREV+I+G4    4262.935     9   8543.870     8544.100     8585.975
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
223  cpREV+F+G4    4247.364     27  8548.727     8550.698     8675.042
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
224  cpREV+F+I+G4  4246.056     28  8548.113     8550.233     8679.106
Akaike Information Criterion:           cpREV+I+G4
Corrected Akaike Information Criterion: cpREV+I+G4
Bayesian Information Criterion:         cpREV+G4
Best-fit model: cpREV+G4 chosen according to BIC

All model information printed to results/supermatrix/supermatrix.protein.fa.model.gz
CPU time for ModelFinder: 6.931 seconds (0h:0m:6s)
Wall-clock time for ModelFinder: 0.623 seconds (0h:0m:0s)
Generating 1000 samples for ultrafast bootstrap (seed: 214413)...

NOTE: 1 MB RAM (0 GB) is required!
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Estimate model parameters (epsilon = 0.100)
1. Initial log-likelihood: -4264.235
Optimal log-likelihood: -4264.235
Gamma shape alpha: 0.255
Parameters optimization took 1 rounds (0.001 sec)
Wrote distance file to... 
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.00135303 secs using 1073% CPU
Computing ML distances took 0.001670 sec (of wall-clock time) 0.017070 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 8.89301e-05 secs using 584.7% CPU
Constructing RapidNJ tree: done in 0.000169039 secs using 619.4% CPU
Computing RapidNJ tree took 0.000348 sec (of wall-clock time) 0.002361 sec (of CPU time)
Log-likelihood of RapidNJ tree: -4264.235
--------------------------------------------------------------------
|             INITIALIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Generating 99 parsimony trees... 0.024 second
Computing log-likelihood of 4 initial trees ... 0.002 seconds
Current best score: -4264.235

Do NNI search on 5 best initial trees
Optimizing NNI: done in 0.00135994 secs using 990.6% CPU
Optimizing NNI: done in 0.00318789 secs using 1100% CPU
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Optimizing NNI: done in 0.00476193 secs using 878.1% CPU
Optimizing NNI: done in 0.00316215 secs using 744.9% CPU
Finish initializing candidate tree set (5)
Current best tree score: -4264.235 / CPU time: 0.048
Number of iterations: 5
--------------------------------------------------------------------
|               OPTIMIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Optimizing NNI: done in 0.00934887 secs using 1001% CPU
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Iteration 10 / LogL: -4264.242 / Time: 0h:0m:0s
Optimizing NNI: done in 0.0239131 secs using 894.6% CPU
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Iteration 20 / LogL: -4264.242 / Time: 0h:0m:0s
Optimizing NNI: done in 0.00525689 secs using 948.5% CPU
Optimizing NNI: done in 0.00523305 secs using 788.6% CPU
Optimizing NNI: done in 0.00390196 secs using 1036% CPU
Optimizing NNI: done in 0.00564504 secs using 1009% CPU
UPDATE BEST LOG-LIKELIHOOD: -4264.235
Optimizing NNI: done in 0.005018 secs using 861.1% CPU
Optimizing NNI: done in 0.00205803 secs using 1002% CPU
Optimizing NNI: done in 0.00287795 secs using 1153% CPU
UPDATE BEST LOG-LIKELIHOOD: -4264.235
Optimizing NNI: done in 0.00337601 secs using 1200% CPU
Optimizing NNI: done in 0.00861883 secs using 1079% CPU
Optimizing NNI: done in 0.00386286 secs using 835.5% CPU
Iteration 30 / LogL: -4264.236 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00143409 secs using 1127% CPU
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Optimizing NNI: done in 0.0079422 secs using 1094% CPU
Iteration 40 / LogL: -4264.254 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.003824 secs using 990.5% CPU
Optimizing NNI: done in 0.00415206 secs using 957.5% CPU
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Optimizing NNI: done in 0.00140095 secs using 1065% CPU
Iteration 50 / LogL: -4270.190 / Time: 0h:0m:0s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -4296.814
Optimizing NNI: done in 0.00503516 secs using 1160% CPU
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Optimizing NNI: done in 0.00449109 secs using 977.5% CPU
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Optimizing NNI: done in 0.0049901 secs using 1085% CPU
Iteration 60 / LogL: -4264.247 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00677419 secs using 999.9% CPU
Optimizing NNI: done in 0.00764298 secs using 960.4% CPU
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Optimizing NNI: done in 0.00393391 secs using 731.8% CPU
Iteration 70 / LogL: -4264.254 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00609422 secs using 883.7% CPU
Optimizing NNI: done in 0.00515103 secs using 910.2% CPU
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Optimizing NNI: done in 0.00299907 secs using 1024% CPU
Iteration 80 / LogL: -4269.635 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.006253 secs using 1004% CPU
Optimizing NNI: done in 0.0050211 secs using 979.3% CPU
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Optimizing NNI: done in 0.00663304 secs using 1091% CPU
Optimizing NNI: done in 0.00516486 secs using 938.4% CPU
Iteration 90 / LogL: -4264.269 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00176215 secs using 1028% CPU
Optimizing NNI: done in 0.00515199 secs using 985.7% CPU
Optimizing NNI: done in 0.00509691 secs using 954.4% CPU
Optimizing NNI: done in 0.007092 secs using 1128% CPU
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Optimizing NNI: done in 0.00493693 secs using 1172% CPU
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Optimizing NNI: done in 0.00663185 secs using 1016% CPU
Optimizing NNI: done in 0.00308299 secs using 829.1% CPU
Iteration 100 / LogL: -4264.244 / Time: 0h:0m:1s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -4287.435
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 0.00684404 secs using 899.4% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:0m:1s

--------------------------------------------------------------------
|                    FINALIZING TREE SEARCH                        |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -4264.235
Optimal log-likelihood: -4264.235
Gamma shape alpha: 0.255
Parameters optimization took 1 rounds (0.001 sec)
BEST SCORE FOUND : -4264.235
Creating bootstrap support values...
Split supports printed to NEXUS file results/supermatrix/supermatrix.protein.fa.splits.nex
Total tree length: 0.657

Total number of iterations: 101
CPU time used for tree search: 10.343 sec (0h:0m:10s)
Wall-clock time used for tree search: 1.006 sec (0h:0m:1s)
Total CPU time used: 10.876 sec (0h:0m:10s)
Total wall-clock time used: 1.055 sec (0h:0m:1s)

Computing bootstrap consensus tree...
Reading input file results/supermatrix/supermatrix.protein.fa.splits.nex...
5 taxa and 9 splits.
Consensus tree written to results/supermatrix/supermatrix.protein.fa.contree
Reading input trees file results/supermatrix/supermatrix.protein.fa.contree
Log-likelihood of consensus tree: -4264.235

Analysis results written to: 
  IQ-TREE report:                results/supermatrix/supermatrix.protein.fa.iqtree
  Maximum-likelihood tree:       results/supermatrix/supermatrix.protein.fa.treefile
  Likelihood distances:          results/supermatrix/supermatrix.protein.fa.mldist

Ultrafast bootstrap approximation results written to:
  Split support values:          results/supermatrix/supermatrix.protein.fa.splits.nex
  Consensus tree:                results/supermatrix/supermatrix.protein.fa.contree
  Screen log file:               results/supermatrix/supermatrix.protein.fa.log

ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/supermatrix/supermatrix.protein.fa.treefile -m "cpREV+G4{0.255214}" --length 795

Date and Time: Fri Jan 19 23:44:31 2024

@article{10.1093/molbev/mst010,
    author = "Katoh, Kazutaka and Standley, Daron M.",
    title = "{MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability}",
    journal = "Molecular Biology and Evolution",
    volume = "30",
    number = "4",
    pages = "772-780",
    year = "2013",
    month = "01",
    abstract = "{We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/mst010",
    url = "https://doi.org/10.1093/molbev/mst010",
    eprint = "https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf"
}

@article{Emms2019,
    author = "Emms, David M. and Kelly, Steven",
    type = "Journal Article",
    title = "OrthoFinder: phylogenetic orthology inference for comparative genomics",
    journal = "Genome Biology",
    number = "1",
    doi = "10.1186/s13059-019-1832-y",
    volume = "20",
    pages = "238",
    url = "https://doi.org/10.1186/s13059-019-1832-y",
    year = "2019",
    abstract = "Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.",
    isbn = "1474-760X",
    DA = "2019/11/14"
}

@article{10.1371/journal.pbio.3001007,
    author = "Steenwyk, Jacob L. and Buida, III, Thomas J. and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    doi = "10.1371/journal.pbio.3001007",
    journal = "PLOS Biology",
    publisher = "Public Library of Science",
    title = "ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference",
    year = "2020",
    month = "12",
    volume = "18",
    url = "https://doi.org/10.1371/journal.pbio.3001007",
    pages = "1-17",
    abstract = "Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.",
    number = "12"
}

@article{Steenwyk_2021,
    author = "Steenwyk, Jacob L. and Buida, Thomas J. and Gon{\c{c}}alves, Carla and Goltz, Dayna C. and Morales, Grace and Mead, Matthew E. and LaBella, Abigail L. and Chavez, Christina M. and Schmitz, Jonathan E. and Hadjifrangiskou, Maria and Li, Yuanning and Rokas, Antonis",
    doi = "10.1101/2021.10.02.462868",
    url = "https://doi.org/10.1101\%2F2021.10.02.462868",
    year = "2021",
    month = "oct",
    journal = "biorxiv",
    publisher = "Cold Spring Harbor Laboratory",
    title = "{BioKIT}: a versatile toolkit for processing and analyzing diverse types of sequence data"
}

@article{10.1093/molbev/msx281,
    author = "Hoang, Diep Thi and Chernomor, Olga and von Haeseler, Arndt and Minh, Bui Quang and Vinh, Le Sy",
    title = "{UFBoot2: Improving the Ultrafast Bootstrap Approximation}",
    journal = "Molecular Biology and Evolution",
    volume = "35",
    number = "2",
    pages = "518-522",
    year = "2017",
    month = "10",
    abstract = "{The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msx281",
    url = "https://doi.org/10.1093/molbev/msx281",
    eprint = "https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf"
}

@article{10.1093/bioinformatics/btab096,
    author = "Steenwyk, Jacob L and Buida, Thomas J, III and Labella, Abigail L and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    title = "{PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data}",
    journal = "Bioinformatics",
    volume = "37",
    number = "16",
    pages = "2325-2331",
    year = "2021",
    month = "02",
    abstract = "{Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock and collapsing bipartitions (internal branches) with low support.To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene–gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining and deriving biological meaning from increasingly large phylogenomic datasets.PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/) and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT).Supplementary data are available at Bioinformatics online.}",
    issn = "1367-4803",
    doi = "10.1093/bioinformatics/btab096",
    url = "https://doi.org/10.1093/bioinformatics/btab096",
    eprint = "https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf"
}

@article{eaton_toytree_2020,
    author = "Eaton, Deren A. R.",
    title = "Toytree: {A} minimalist tree visualization and manipulation library for {Python}",
    volume = "11",
    doi = "10.1111/2041-210X.13313",
    journal = "Methods in Ecology and Evolution",
    year = "2020",
    pages = "187--191"
}

@article{10.1093/molbev/msaa015,
    author = "Minh, Bui Quang and Schmidt, Heiko A and Chernomor, Olga and Schrempf, Dominik and Woodhams, Michael D and von Haeseler, Arndt and Lanfear, Robert",
    title = "{IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era}",
    journal = "Molecular Biology and Evolution",
    volume = "37",
    number = "5",
    pages = "1530-1534",
    year = "2020",
    month = "02",
    abstract = "{IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msaa015",
    url = "https://doi.org/10.1093/molbev/msaa015",
    eprint = "https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf"
}

@article{Kalyaanamoorthy2017,
    author = "Kalyaanamoorthy, Subha and Minh, Bui Quang and Wong, Thomas K F and von Haeseler, Arndt and Jermiin, Lars S",
    type = "Journal Article",
    title = "ModelFinder: fast model selection for accurate phylogenetic estimates",
    journal = "Nature Methods",
    number = "6",
    doi = "10.1038/nmeth.4285",
    volume = "14",
    pages = "587--589",
    url = "https://doi.org/10.1038/nmeth.4285",
    year = "2017",
    abstract = "ModelFinder is a fast model-selection method that greatly improves the accuracy of phylogenetic estimates.",
    issn = "1548-7105",
    DA = "2017/06/01"
}

@article{Zhang2018,
    author = "Zhang, Chao and Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash",
    type = "Journal Article",
    title = "ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees",
    journal = "BMC Bioinformatics",
    number = "6",
    doi = "10.1186/s12859-018-2129-y",
    volume = "19",
    pages = "153",
    url = "https://doi.org/10.1186/s12859-018-2129-y",
    year = "2018",
    abstract = "Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.",
    issn = "1471-2105",
    DA = "2018/05/08"
}